Automated detection and classification of rice crop diseases using advanced image processing and machine learning techniques. Traitement Du Signal 41, 739–752. doi: 10.18280/ts.410216 ...

These findings suggest that ChlF imaging is a powerful tool for early, non-invasive detection of rice fungal diseases, facilitating timely disease management and intervention.

Deep learning breakthrough enhances crop disease detection across lab and field Plant diseases remain a major threat to food security and agricultural productivity, especially in resource-constrained ...

Researchers have made significant progress in the field of artificial intelligence (AI) by applying deep learning techniques to automate the detection and classification of crop leaf diseases.

AI crop disease detection systems can also alert the farmer at a point when a set ‘infection threshold’ is met, automatically recommending the right crop fungicide or other product to be sprayed when ...

Most current crop diseases present small targets, dense numbers, occlusions and similar appearance of different diseases, and the current target detection algorithms are not effective in identifying ...

This paper proposed an intelligent plant disease detection system that uses the modified Alexnet. As a convolutional neural network (CNN), Alexnet is good at objection detection from images. However, ...

Recently, Image processing started to set its path in agriculture after its successful application in various fields and has now. In this paper, we have analyzed the various applications of image ...